Recommendations based on private data using a dynamically deployed pre-filter

ABSTRACT

Methods and systems for targeted advertisement include transmitting a pre-filter to a user device, responsive to contextual information supplied by the user device to determine one or more inferences based on physical browsing information, collected at the user device, in compliance with one or more privacy policies of the user. One or more targeted advertisements are determined, using a processor, based on the one or more inferences. The one or more targeted advertisements are transmitted to the user device.

BACKGROUND

Technical Field

The present invention relates to advertising and, more particularly, toproviding advertising recommendations to users based on physicalbrowsing information in a manner that does not compromise user-privacyyet maximizes utility of the data to the ad-provider.

Description of the Related Art

Targeted advertising based on online browsing data of users is wellestablished. However, recent advances in portable electronics haveprovided the ability to collect information regarding users' physicalbrowsing data. Physical browsing information includes data based onusers' behavior in physical stores and other locations. Physicalbrowsing data includes locations that the user visited, user videos,pictures, audio, etc., and provides a wealth of information if usedeffectively for targeted advertisements. However, physical browsing datais very sensitive, as it contains private information about the users.

SUMMARY

A method for targeted advertisement includes transmitting a pre-filterto a user device, responsive to contextual information supplied by theuser device to determine one or more inferences based on physicalbrowsing information, collected at the user device, in compliance withone or more privacy policies of the user. One or more targetedadvertisements are determined, using a processor, based on the one ormore inferences. The one or more targeted advertisements are transmittedto the user device.

A method for targeted advertisement includes transmitting a context fora user device to an advertisement provider. One or more inferences aredetermined using a processor by applying a pre-filter, receivedresponsive to the transmitted context, to collected physical browsinginformation, collected at the user device, in accordance with one ormore privacy policies of a user. The one or more inferences aretransmitted to an advertisement provider. One or more targetedadvertisements, received from the advertisement provider are in responseto the one or more inferences, are displayed.

A system for targeted advertisement includes a server pre-filter moduleconfigured to transmit a pre-filter to a user device, responsive tocontextual information supplied by the user device, to determine one ormore inferences based on physical browsing information, collected at theuser device, in compliance with one or more privacy policies of theuser. A recommendation module includes a processor configured todetermine one or more targeted advertisements based on the one or moreinferences. An advertisement module is configured to transmit the one ormore targeted advertisements to the user.

A system for targeted advertisement includes a client pre-filter modulethat has a processor configured to transmit a context for a user deviceto an advertisement provider and to determine one or more inferences byapplying a pre-filter, received responsive to the transmitted context,to collected physical browsing information, collected at the userdevice, in accordance with one or more privacy policies of a user and totransmit the one or more inferences to an advertisement provider. Anadvertisement module is configured to display one or more targetedadvertisements received from the advertisement provider in response tothe one or more inferences.

These and other features and advantages will become apparent from thefollowing detailed description of illustrative embodiments thereof,which is to be read in connection with the accompanying drawings.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The disclosure will provide details in the following description ofpreferred embodiments with reference to the following figures wherein:

FIG. 1 is a block/flow diagram of a method for targeted advertisement inaccordance with the present principles;

FIG. 2 is a block diagram of a system for targeted advertisement inaccordance with the present principles;

FIG. 3 is a block diagram of a user device part of a system for targetedadvertisement in accordance with the present principles;

FIG. 4 is a block diagram of an ad-provider part of a system fortargeted advertisement in accordance with the present principles;

FIG. 5 is a diagram of a cloud computing environment according to thepresent principles; and

FIG. 6 is a diagram of abstraction model layers according to the presentprinciples.

DETAILED DESCRIPTION

Embodiments of the present invention deploy a pre-filter onto users'devices that filters personal and potentially sensitive informationbefore providing relevant inferences to the advertiser. The pre-filterthereby identifies information that is important to the advertiser—e.g.,that the user visits a certain store or purchases a certainbrand—without providing actual location data or distributing the user'spictures or videos. Because the sensitive information itself neverleaves the user's device, the user's privacy is protected.

The pre-filter is deployed to the user's device on a case-by-case basis,with the specific inferences being formed depending on the particularcontext. For example, the type of physical data, the type of store, andthe type of advertisers are all contributing factors toward determiningwhat pre-filter to use. The user establishes preferences for thepre-filter, which determine what sorts of data the pre-filter can accessand send back.

The pre-filter thereby allows advertisement providers the ability toleverage physical analytics data for better targeting. This benefitsusers by tailoring advertisements to their tastes, providing discountsand coupons for products and services that the users will actually findinteresting. In addition, the use of a pre-filter decreases the amountof information that is transmitted back to the advertisement provider,as it puts the processing at the user's device and transmits only theuseful part of the information. This further safeguards the user'sprivacy, as the pre-filter operates only within the boundariesestablished by the user's preferences.

Physical browsing data is collected by users' personal portable devices,which collect data as users go about their lives. The data may includevideo, audio, wireless network scans, inertial sensors, etc. All of thisinformation can be used to infer the context of the user. Wearables andother sensing devices then detect if the data is useful for advertisingbased on the context of the user, and only useful data is acted upon.For example, data collected when the user is in a meeting is less likelyto be useful for advertising when compared to data collected when theuser is in a shopping mall.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Referring now to the drawings in which like numerals represent the sameor similar elements and initially to FIG. 1, a method for providingadvertising content is shown. Block 102 collects physical browsing dataon a user's device, such as a smart phone, wearable device, or tablet.This may include collecting the information using on-board sensors builtinto the device (e.g., from a camera, wireless network interface,microphone, GPS, or other internal or external sensor) or from aperipheral that has dedicated sensors (e.g., a user's personalfitness/biometric monitor) and sends information to the user's device.In one specific example, the user's wearable device takes a picture in aparticular store and transmits that picture to the user's smartphone.Block 102 potentially has access to all data physically available to thedevice, which makes it possible to identify the user's physical browsingactivities. Because this information can be quite sensitive, the usermay not wish to share the raw data itself, but may nonetheless want toreceive promotional information, such as discounts and coupons, from anad provider.

Block 104 optionally sends the device's status to the ad provider. Thisstatus information may include, e.g., remaining battery power, resourceconsumption, and processing capabilities of the device. Notably, thisinformation is shared only in accordance with the user's preferences, asthe user may not wish to send additional information to the ad provider.In one example, the user's smart phone is low on battery and thus canrun only limited computations to avoid the risk of affecting the user'sability to use their device. This information can be used by the adprovider to determine whether to send a computationally intensivepre-filter or to instead send a pre-filter with relatively lowcomputational requirements. If the user does not want to send additionaldata to facilitate choosing a pre-filter, all the device sends is a“context,” for example stating that the user is at a shopping mall, andthe type of data available, for example image data, so that thead-provider is informed when to deploy the pre-filter and what type ofprocessing is needed.

Block 106 deploys a pre-filter to the user's device on demand. Thepre-filter that is deployed may be selected based on a variety offactors including, e.g., the user device's status and the ad provider'sneeds for data (e.g., based on current ad pool, other similar users,etc.). The ad provider deploys the pre-filter best suited to the presentcircumstances to the user's device. In one example, an object detectionpre-filter is deployed to the user's smart phone to recognize objectswithin a given image. In another example, a text recognition pre-filteris deployed to the user's smart phone to locate known brand names withinan image.

Block 108 applies privacy policies to the pre-filter. The user providesa security profile that includes privacy and security preferences. Thesepreferences may be set manually by the user or may, alternatively, beset according to user defaults. In one specific embodiment, the defaultpolicies are selected based on the user's other privacy and securitysettings in, for example, their social media profiles. In addition, thedefault preferences may be guided by their level of social mediaactivity, with status updates that include physical location indicatingthat the user does not wish to protect such information as tightly. Thesettings may include fine-grained controls over privacy, for exampleprohibiting pictures from being sent to ad providers.

Block 110 runs the pre-filter on the collected data. The pre-filter maytake into account a determination of the user's context, for exampledeciding that the information is not relevant due to the user beinglocated at work. In addition, the privacy and security settings affectthe operation of the pre-filter, blocking certain operations and typesof information. The pre-filter's operation on the collected datagenerates one or more inferences about the user's physical browsingdata. These inferences are the output of the pre-filter that is usefulto the advertisement provider in deciding what advertisement to show.Inferences are useful to help the ad provider know the interests of theuser and can be in the form of locations that the user has visited,objects that the user has seen, and activities that the user hasperformed. In one example, the pre-filter may determine that the user islooking at a watch.

Block 112 verifies the output of the pre-filter. This is accomplished byreconstructing the message as specified by the pre-filter and checkingthat the output complies with security and privacy policies.Verification checks, for example, that the message only includes thecomputed inferences and does not include more sensitive information.Following the above example, block 112 would determine that the outputincludes the inference that the user is interested in watches, but doesnot include any picture. If the output is compliant with the policies,block 114 then sends the verified output to the ad provider.

Block 115 computes a recommendation of a targeted advertisement based onthe pre-filtered output (that output being referred to herein as an“inference”) at the advertisement provider. The ad provider sends therecommended advertisements to the user based on the output in block 116.The targeted advertisement may be based on one or more recommendationprocesses which may be proprietary to the ad provider. Thus the adprovider runs the recommendation process on its own device (or in acloud computing resource allocated to the ad-provider). This also allowsthe ad provider to leverage other information that is available, forexample by cross-referencing with other users' similar profiles, thecurrent advertiser pool, etc. Thus, in the above example, block 116 mayprovide the user with a targeted advertisement that includes a watchcoupon.

In one particular embodiment, block 116 may employ physical browsinginformation from other users as well when serving a targetedadvertisement to the user. This is possible if the other users sharetheir own physical browsing information and that information correlateswith the physical browsing information collected by the user. Forexample, the ad provider may create a general database of trends acrossall users, allowing it to make predictions about the user's behavior andpreferences from seemingly unrelated behaviors. More directly, if theuser is friends with the other users in question and those friends havephysical browsing information that shows they are, for example, outshopping with the user, then the ad provider can draw further inferencesabout the user from the user's friends' physical browsing data.

Referring now to FIG. 2, a diagram of the relationships between the userand the ad provider is shown. A user 202 lives in a physical context200. This context 200 includes every place the user 202 visits, everyphysical activity the user 202 engages in, and every object the user 202interacts with. The user carries one or more personal devices 204. Thesedevices 204 may be, for example, smart phones or tablets, or they may bewearable devices that stand alone or that operate in conjunction withmore powerful devices. As the user 202 interacts with their context 200,the personal device 204 collects information about those interactions.This information may be captured automatically, as in the case oflocation or biometric monitoring devices, or may be collected at theuser's explicit request, such as when the user takes a picture.

In particular, the device(s) 204 record the user's interaction with oneor more objects 208 in the context 200. This object may, for example, bea product for sale or a particular store or location. The device(s) 204collect information about that interaction and apply a pre-filter thatwas supplied by ad provider 206, which is a remotely based systemoperating outside the context 200. The device(s) 204 use the pre-filterto make inferences about the user's physical browsing activitieslocally, such that the collected information itself is not directlysupplied to the ad provider 206, in accordance with privacy and securitypolicies located on the device(s) 204 or in the cloud. Once thedevice(s) 204 produce these inferences, they send the inferences to thead provider 206.

The ad provider 206 then uses the inferences to determine one or moretargeted advertisements. These targeted advertisements are formed insome fashion responsive to the user's interaction with the context 200.In one example, if the pre-filter infers that the user is groceryshopping, the ad provider 206 may supply coupons relating to the user'spreferred brands. If the pre-filter infers that the user is at work, thead provider 206 may supply advertisements relating to getaway vacations.If the user looked at a watch for a long time, then ad provider 206 maysupply a coupon for the watch. The ad provider 206 delivers the targetedadvertisement to one or more of the personal devices 204 or by someother delivery mechanism, such as through email, physical mail, instantmessaging, or through advertisements displayed in a web browser.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Reference in the specification to “one embodiment” or “an embodiment” ofthe present principles, as well as other variations thereof, means thata particular feature, structure, characteristic, and so forth describedin connection with the embodiment is included in at least one embodimentof the present principles. Thus, the appearances of the phrase “in oneembodiment” or “in an embodiment”, as well any other variations,appearing in various places throughout the specification are notnecessarily all referring to the same embodiment.

It is to be appreciated that the use of any of the following “/”,“and/or”, and “at least one of”, for example, in the cases of “A/B”, “Aand/or B” and “at least one of A and B”, is intended to encompass theselection of the first listed option (A) only, or the selection of thesecond listed option (B) only, or the selection of both options (A andB). As a further example, in the cases of “A, B, and/or C” and “at leastone of A, B, and C”, such phrasing is intended to encompass theselection of the first listed option (A) only, or the selection of thesecond listed option (B) only, or the selection of the third listedoption (C) only, or the selection of the first and the second listedoptions (A and B) only, or the selection of the first and third listedoptions (A and C) only, or the selection of the second and third listedoptions (B and C) only, or the selection of all three options (A and Band C). This may be extended, as readily apparent by one of ordinaryskill in this and related arts, for as many items listed.

Referring now to FIG. 3, a user's personal device 204 is shown. Thepersonal device 204 includes a hardware processor 302 and a memory 304.The personal device 204 further includes one or more sensors 306. Itshould be understood that these sensors may be physically integratedwith the personal device 204 itself or may, alternatively, be externalsensors that communicate with the physical device 204 by way of wired orwireless communications. In one specific example, the personal device204 may be a smart phone that includes its own sensors (such as, e.g., aGPS, accelerometer, and camera) and also communicates with an externalsensor device such as a wearable biometric monitor.

The personal device 204 includes one or more functional modules. Thesemodules may be implemented as software that is executed by the hardwareprocessor 302 or, alternatively, they may be implemented discretely ortogether by dedicated hardware components in the form of, e.g., one ormore application specific integrated chips or field programmable gatearrays.

The personal device 204 captures and stores information from the sensor306 in the memory 304. The personal device 204 also stores a privacy andsecurity policy 308 in its memory 304. A notification module 314 sendsinformation to the advertisement provider 206 to inform it that the useris collecting physical browsing information and that a pre-filter can beused. The notification module 314 includes contextual information, whichmay include some minor, non-private piece of the physical browsinginformation, to help guide the advertisement provider 206 in determiningwhich pre-filter to send. A client pre-filter module 310 receives apre-filter from an ad provider 206 and applies it to the stored sensorinformation to generate one or more inferences. Based on theseinferences, the client pre-filter module 310 creates a message or reportfor the advertisement provider 206 and verifies that the inferencescomply with the stored privacy policy 308. The client pre-filter module310 then sends the message with its inferences to the ad provider 206.

The personal device 204 also includes a client advertisement module 312.The client advertisement module 312 receives communications from theadvertisement provider 206 that include targeted advertisements for theuser. The client advertisement module 312 instructs the user personaldevice 204 to provide these targeted advertisements to the user through,for example, a graphical user interface.

Referring now to FIG. 4, an advertisement provider system 206 is shown.It should be understood that the advertisement provider system 206 may asingle, centralized device or may be implemented in a decentralizedfashion in, for example, a cloud computing environment. As above, theadvertisement provider system 206 includes a hardware processor 402 anda memory 404. The advertisement provider system 206 includes one or morefunctional modules. These modules may be implemented as software that isexecuted by the hardware processor 402 or, alternatively, they may beimplemented discretely or together by dedicated hardware components inthe form of, e.g., one or more application specific integrated chips orfield programmable gate arrays.

The advertisement provider system 206 stores a pool of availableadvertisements 406 in its memory 404. The system 206 providespre-filters to a user personal device 204 in accordance with userbehavior and the available advertisements in the advertisement pool 406.For example, if an advertisement is to be targeted to users with aninterest in watches, the server pre-filter module 408 may supplypre-filters that perform object detection and that search textinformation for references to watches. This will help save memory onuser's device as the models needed for looking only at a sub-set ofinteresting objects will be smaller. The server pre-filter module 408will also be more computationally efficient as it looks only forspecific data.

Based on inferences received from the pre-filters on the user personaldevices 204, a recommendation module 410 determines whether to send anadvertisement to a particular user device 204. A server advertisementmodule 412 then selects an appropriate advertisement from theadvertisement pool 406 and transmits the targeted advertisement to theuser personal device 204.

Referring now to FIG. 5, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 6 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 6, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 5) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 6 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and targeted advertisement recommendation 96.

Having described preferred embodiments of recommendations based onprivate data using a dynamically deployed pre-filter (which are intendedto be illustrative and not limiting), it is noted that modifications andvariations can be made by persons skilled in the art in light of theabove teachings. It is therefore to be understood that changes may bemade in the particular embodiments disclosed which are within the scopeof the invention as outlined by the appended claims. Having thusdescribed aspects of the invention, with the details and particularityrequired by the patent laws, what is claimed and desired protected byLetters Patent is set forth in the appended claims.

1. A method for targeted advertisement, comprising: transmitting apre-filter to a user device, responsive to contextual informationsupplied by the user device to determine one or more inferences based onphysical browsing information, collected at the user device, incompliance with one or more privacy policies of the user; determining,using a processor, one or more targeted advertisements based on the oneor more inferences; and transmitting the one or more targetedadvertisements to the user device.
 2. The method of claim 1, wherein thecontextual information comprises one or more pieces of informationregarding the user's location and a type of data collected by the userdevice that is permitted by the privacy policies of the user.
 3. Themethod of claim 1, wherein the contextual information identifies one ormore types of physical browsing information collected at the user deviceand the pre-filter for transmission is selected in accordance with acorresponding type of physical browsing information.
 4. The method ofclaim 1, wherein the one or more inferences comprise analysis outputsbased on the physical browsing information and do not include thephysical browsing information itself.
 5. A computer readable storagemedium comprising a computer readable program for targetedadvertisement, wherein the computer readable program when executed on acomputer causes the computer to perform the steps of claim
 1. 6. Amethod for targeted advertisement, comprising: transmitting a contextfor a user device to an advertisement provider; determining one or moreinferences using a processor by applying a pre-filter, receivedresponsive to the transmitted context, to collected physical browsinginformation, collected at the user device, in accordance with one ormore privacy policies of a user; transmitting the one or more inferencesto an advertisement provider; and displaying one or more targetedadvertisements received from the advertisement provider in response tothe one or more inferences.
 7. The method of claim 6, wherein thecontextual information comprises one or more pieces of informationregarding the user's location and a type of data collected by the userdevice that is permitted by the privacy policies of the user.
 8. Themethod of claim 6, wherein the contextual information identifies one ormore types of physical browsing information collected at the user deviceand the pre-filter corresponds to a type of physical browsinginformation.
 9. The method of claim 6, wherein the one or moreinferences comprise analysis outputs based on the physical browsinginformation and do not include the physical browsing information itself.10. A computer readable storage medium comprising a computer readableprogram for targeted advertisement, wherein the computer readableprogram when executed on a computer causes the computer to perform thesteps of claim
 6. 11. A system for targeted advertisement, comprising: aserver pre-filter module configured to transmit a pre-filter to a userdevice, responsive to contextual information supplied by the userdevice, to determine one or more inferences based on physical browsinginformation, collected at the user device, in compliance with one ormore privacy policies of the user; a recommendation module comprising aprocessor configured to determine one or more targeted advertisementsbased on the one or more inferences; and an advertisement moduleconfigured to transmit the one or more targeted advertisements to theuser.
 12. The system of claim 11, wherein the contextual informationcomprises one or pieces of information regarding the user's location anda type of data collected by the user device that is permitted by theprivacy policies of the user.
 13. The system of claim 11, wherein thecontextual information identifies one or more types of physical browsinginformation collected at the user device and the pre-filter fortransmission is selected in accordance with a corresponding type ofphysical browsing information.
 14. The system of claim 11, wherein theone or more inferences comprise analysis outputs based on the physicalbrowsing information and do not include the physical browsinginformation itself.
 15. The system of claim 11, wherein the physicalbrowsing information comprises image data collected by one or morecameras at the user device.
 16. A system for targeted advertisement,comprising: a client pre-filter module comprising a processor configuredto transmit a context for a user device to an advertisement provider andto determine one or more inferences by applying a pre-filter, receivedresponsive to the transmitted context, to collected physical browsinginformation, collected at the user device, in accordance with one ormore privacy policies of a user and to transmit the one or moreinferences to an advertisement provider; and an advertisement moduleconfigured to display one or more targeted advertisements received fromthe advertisement provider in response to the one or more inferences.17. The system of claim 16, wherein the contextual information comprisesone or more pieces of information regarding the user's location and atype of data collected by the user device that is permitted by theprivacy policies of the user.
 18. The system of claim 16, wherein thecontextual information identifies one or more types of physical browsinginformation collected at the user device and the pre-filter correspondsto a type of physical browsing information.
 19. The system of claim 16,wherein the one or more inferences comprise analysis outputs based onthe physical browsing information and do not include the physicalbrowsing information itself.
 20. The system of claim 16, wherein thephysical browsing information comprises image data collected by one ormore cameras at the user device.